AXIOM BOINC SESSION RESULTS (Part 3 Consolidation)
Session timestamp: 2026-03-04 14:30 (America/Denver)
Source logs: validate_2026-03-04_1428.txt + run_2026-03-03_1352.log

RESULTS REVIEWED AND CREDIT AWARDED (Part 1)
- Reviewed and credited 20 completed success results (non-cumulative; DB remains source of truth).
- Payload audit: 20/20 resolved upload files parsed as valid JSON with `experiment_result` objects.
- Elapsed-time profile of credited batch: min=822.12s, median=846.51s, max=1022.45s.
- Total credit awarded this session: 405 (under 10,000 cap).
- Per-user credit additions: Orange Kid +100; Steve Dodd +84; ChelseaOilman +80; WTBroughton +61; Armin Gips +40; PyHelix +20; vanos0512 +20.
- Counter updates from validation log: credited_count 1678 -> 1698; total_results_count 1974 -> 1979.

STUCK/BROKEN TASK CLEANUP
- Stuck dead-host cleanup (>12h running, host silent >6h): 0 aborted.
- Hard-ceiling cleanup (>48h running): 0 aborted.
- Broken-experiment screening (recent 72h nonzero-elapsed failures, CPU+GPU): no broad unsent/in-progress abort required.
- Retirement pass in Part 2: `ABORT_TOTAL=0` (retirement candidates existed but unsent backlog was zero at action time).

DEPLOYMENT SUMMARY (Part 2)
CPU DEPLOYMENT
- Deployment model: host-targeted queue fill to ~3x CPU slots; hosts with RAM <6GB skipped.
- CPU hosts seen: 81.
- CPU hosts skipped for low RAM: 2.
- CPU workunits created: 2937.
- CPU scripts used:
  1. wd_batchnoise_interaction.py (newly added in this run)
  2. wd_labelsmooth_interaction.py

GPU CHECKPOINT
- GPU deployment run initiated with GPU-aware script set:
  1. wd_curvature_trigger_gpu.py
  2. wd_timing_scale_gpu.py
- Run log ended with manual interrupt (`^C`) before final GPU counters were emitted.
- GPU hosts deployed (from this run log): not recorded due interruption.
- GPU workunits deployed (from this run log): not recorded due interruption.

NEW EXPERIMENTS DESIGNED + NOVELTY CHECK NOTES
- New experiment script created and syntax-checked: `wd_batchnoise_interaction.py` (remote `python3 -m py_compile` returned `OK`).
- Intended mechanism test: whether late weight-decay gain is stronger under high-noise/small-batch training than large-batch training.
- Novelty-check search queries recorded in run log:
  1. weight decay batch size interaction neural networks
  2. arxiv weight decay batch size interaction deep learning
  3. Scheduled Weight Decay paper arxiv 2021
  4. site:arxiv.org weight decay label smoothing interaction
  5. site:arxiv.org adaptive weight decay deep neural networks
  6. site:arxiv.org batch size weight decay generalization
  7. arxiv 1711.05101 decoupled weight decay regularization
- Novelty position used for deployment planning: no exact prior match was documented in-session for this specific late-WD x batch-noise interaction protocol and metric bundle.

KEY SCIENTIFIC FINDINGS
1. All 20 newly credited results were science-bearing (`experiment_result`) with zero parse failures, supporting current pipeline integrity.
2. Credited runtimes clustered in a stable band (~822-1022s), consistent with healthy throughput across active experiment families.
3. No active nonzero-elapsed crash cluster (CPU or GPU) was detected in the 72h screening window, so broad abort was not warranted.
4. Over-seeded retirement candidates remained unsent-clean at deployment time (ABORT_TOTAL=0), indicating queue pressure came from active lines rather than retired-family backlog.